A Deep Learning-Based Approach for Shared Autonomous Vehicle Rebalancing Under Real-Time Traffic Congestion

Existing approaches for rebalancing shared autonomous vehicles (SAVs) typically employ a two-step procedure, in which the future travel demand is first predicted and then fed to rebalance decision-making process. As a result, prediction errors are accumulated and propagated to the second stage, leading to inferior rebalance performance. To tackle this problem, this work proposes a deep learning-based approach to map real-time system state to vehicle rebalance decision in one step to eliminate decoupling errors. The deep learning-based method is based on a cascaded long short-term memory (LSTM) and bidirectional gated recurrent unit (BiGRU) network. Training data set is generated by applying a mathematical rebalance model to a simulation model, which allows the emergence of congestion. Numerical experiments are performed with the taxi travel data from the Manhattan road network. Compared with the mathematical rebalance model, the proposed approach achieves better performance in terms of the average passenger waiting time, total vehicle miles traveled, total system operating cost, and congestion level. The independent LSTM model has a larger impact on congestion reduction (up to 42.4%) but leads to a large portion of unsatisfied demand, while the proposed integrated approach achieves a better trade-off between congestion alleviation and level of service. These findings provide practical management insights for SAV fleet operators devoted to formulating sustainable shared on-demand mobility systems.